"What I need to know about bots"

Thorsten Schneider, Consultant

Understanding bot technologies

Voice bots and chatbots architectures

Webinar Series Spring 2020

• March 03, 2020Extensive functionalities with Cisco's smart omni-channel contact centersCisco Contact Center full coverage with live voting and demos. Determine the topics; from the agent desktop strategy to Cloud CC and enriched AI contact centers.

• March 04, 2020Live Vote! Your top 3 Omnichannel Desktop CRM Integration Challenges and how to approach them#Salesforce #Dynamics #ServiceNow #SAP #Oracle #OwnCRM

• March 12, 2020Home Office: Mobile Agent in ActionThis is a technical Webinar. We will demonstrate what the mobile agent function offers and how you can configure it yourself.

• March 19, 2020 | 09:00am

What I need to know about bots.In this webinar, you will learn how voice and chatbots work. What are the differences between them? You’ll learn all about intents and entities. Which solutions are available today?

• March 24, 2020 | 09:00amWhat's new in CCE 12.5?Learn more about Finesse 12.5 features. What is the SMC Cockpit? Do you already know the new “Cisco Analyzer” reporting engine?

• April 02, 2020 | 09:00amWhat's new in the Cisco omnichannel suite? What is Enterprise Chat and Email (ECE)? How can you extend Cisco ECE with more features?

• April 16, 2020 | 09:00amNews around SMC 7.0. Better understand where SMC can help you in your daily business as a supervisor. What is our Supervisor Management Console vision?

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Agenda

• Understanding Bots

• Chatbot Solutions

• Voice Bot Solutions

• Demo

• Agent A.I. Assistant Services

• Typical Project Approach

Understanding Bots

Overview of the process

Input/Output Management Processing

Customer

Channels

Call

Web

Mobile

Video

Mail

WebChat

Social

TextAnalytics

Context

Knowledge Management

Analytics

Bot Routing & Agents

Virtual Agent

Human Agent

Bot to human hand-off

Customer Workflow

Universal Queue

Important Components of a Bot

Component Description

NLU Natural Language Understanding. This component takes text input and can determine the intent of the input and also identify the entities.

Dialog Management incl. Orchestration

Component predicts the next best action/question to identify and fill all entities also using the customer’s backend system. For example, if an entity is not filled, this component will ask a question to get the entity filled. Also it controls the conversation and determines if a handover to a contact center with skilled human agents is needed or “finishes” the request in the backend, without agent intervention.

TTS Text to Speech. This component is used to synthesize the output of the NLP into speech.

ASR/STT Automatic Speech Recognition/Speech to Text. These components are only used in Voice Bots for transforming spoken context into text.

Customers Backend System holding customer data, order processing systems, ERP, CRM etc.

Microsoft LUIS (NLU)

Components for a Chatbot

Dialog Management & Orchestration

NLU

CustomerBackend

(Customer Data/ Request fulfillment)

Webchat/Social Messaging

connector

Inside the NLUSpeech Recognition

(ASR)

Co

nten

t/ In

tent

word vectors

sentence vectors!

document vectors!

Context/ DialogOrchestration

Content identification

(NLU)T

extS

ynth

eses

Text to Speech(TTS)

Example of Dialog Management & NLU

{ "query": "Book me a flight to Cairo",

"topScoringIntent": { "intent": "BookFlight", "score": 0.9887482 },

"intents": [ {}, "intent": "BookFlight", "score": 0.9887482 { "intent": "None", "score": 0.04272597 }, { "intent": "LocationFinder", "score": 0.0125702191 }, { "intent": "Reminder", "score": 0.00375502417 }, { "intent": "FoodOrder", "score": 3.765154E-07 }, ], „

entities": [ { "entity": "cairo", "type": "Location", "startIndex": 20, "endIndex": 24, "score": 0.956781447 } ] }

NLU Processing

Destination: Cairo

Person?

Start airport?

Date/Time?

Customer:

“Book me a flight to Cairo”

Intent: “BookFlight”

Next best question/action:

“OK, you want to fly to Cairo. Where do you want to start?”

Dialog Management

Natural Language Understanding

Book me a f l ight f rom New York to Budapest today.

Intent: book flight

Entities: Person: Thorsten Schneider, customer ID:118473 (me)

Departure Airport: New York

Destination Airport:Budapest

Flight time: 19.03.2020 (today)

User Authentication

& Transformation through DialogManagement

Entity & Intent Extraction

New York

- JKF - John F. Kennedy International Airport

- LGA - LaGuardia Airport

- EEA - Newark Liberty

- JRB - Downtown Manhattan Heliport

- QNY - East 34th Street Heliport

- JRA - West 30th Street Heliport

- 6N6 - Evers Seaplane Base

Bot Dialog Management is responsible for

filling the entity “destination”.

It asks predefined questions find the correct

airport out of 188 New York airports

Natural Language UnderstandingEntity & Intent Extraction

Dialog Management

Book me a f l ight

Departure airport?

Destination airport?

Flight date?

INTENT

QUESTION

QUESTION

VALIDATIONS

CONDITIONS

HELP & INPUT*

CONFIRMATION*

GOAL WORKFLOW

QUESTION

{ Person: Thorsten (me),

Departure Airport: New York,

Destination Airport: Budapest,

Flight time: 19.03.2020}

API search

Training the NLU Process

Training Material

Data Anonymization (remove names,

adresses, cities etc)

Transf-ormation

from Speech to text

old/existingChat Transscripts

or eMails etc.

Training Material

old/existing Voice Recordings

Elemination of

background noises

Manual Classification

(determine the intent and the

entities)

Train the NLU

Test the NLU

Create a training model & adjust the speech grammar of the ASR engine in order to identify specific product

names etc.

Fine tuningMultiple iterations

New training run

start

Multiple hundreds of training runs needed, usually executed by a subject matter expert within the company

Entities

Intents

Frontend Training(example from Google Dialogflow)

Turn the Chatbot into a Voice Bot

Dialog Management & Orchestration

NLU

CustomerBackend

(Customer Data/ Request fulfillment)

ASRTTS

Chatbot = or ≠ Voice Bot?

NLU

Dia

log

Ma

na

gem

ent &

Orch

estratio

n

Dia

log

Ma

na

gem

ent &

Orch

estratio

n

ASR

TTS

Social Messaging Interface

Customer Backend Data

Voice Bot Challenges

Challenge Solution

Background noises Audio tuning

Language Dialects Train a Statistical Language Model (SLM)

Understand the correct context Grammar Tuning of the ASR & TTSExample: QR Code ≠ Kuh R Kot

“Eh, Ehm, Hm” and usage of fillwords Identification and removal in Dialog Management

Fast dialog conversations flow, customer answers immediately andunprecised via speech

Dialog Manager must “steer” the conversation, to get precise information

> N O " O N E F I T S A L L " S O L U T I O N> N O " O F F T H E S H E L F " S O L U T I O N

" A I " I S N O T A S O F T W A R E P R O D U C T

b+s connects bots with contact centers

ChatbotSolutions

Current ChatbotSolutions

• eGain Virtual Assistant for Cisco ECE

• Individual “any” bot integration, based on ECE

eGain Virtual Assistant for

Cisco ECE

Bot assistant pops upon customer site

Handoff the chat frombot to the agent

Chat Virtual Assistant

eCommerce, Transactional Systems, and Fulfillment

Dialog

Manager

Context

Sentiment

Brand Pref

Intent

Classification

Confidence?

Virtual Assistant

Knowledge

Base

Process

Guidance

Agent escalation

Conversational reasoning

ResolutionMediumLow

High

Advisor

Digital Media

Caller

Individual Bot Integration into

Cisco ECE

• New Finesse ECE frontend in CCE 12.0

• Pop-up on incoming chat

• Enable/disable chat button on website

based on agent availability

• Show expected wait time to get to an

agent

• Open API for social messenger

integration and apps

ECE Chat

Chat BOT Framework

Agent CTI Desktop

NLPTranslation Services

Chat BOT Orchestrator

CRM

Webchat

Voice Bot Solutions

Current Voice Bot Solutions• SemanticEdge

• Microsoft LUIS

• Google Dialogflow

SemanticEdgeVoice Bot

Voice Bot Processing

Calling

Person

Automatic

Transcription

TranscriptionEngine

Text

Manual “post”

transcription and

“post” classification

Statistic AI

classification

DeepLearningClassifier

DeepLearning

NLU

SLM

Text

Source: SemanticEdge

Microsoft Cognitive Services

Voice Bot

using Microsoft LUIS connected via AudioCodes AI Gateway

Agent CTI Desktop

Cisco CVP

Cisco CCE

Escalate Call to agent

with extracted information from

botConsolidated Routing & Reporting Finesse

Customer calling

Cisco “Conversational

IVR”

based onVVB 12.5 Integration with Google Dialogflow

Voice Bot

Conversational IVR with CCE 12.5

Agent CTI Desktop

Cisco CVP

Cisco CCE

Escalate Call to agent

with extracted information from

botConsolidated Routing & Reporting Finesse

Customer calling

DEMOGoogle Dialogflow Voice Bot

Agent A.I.AssistanceServices

• Cisco Answers

• Audio transcriptions for quality assurance & data analysis

• Chat & Voice translational services

Cisco Answers providesthe agent knowledgearticles based on the

the voice conversation

The Finesse “VoiceaIntegration” provides a

transscript of the conversation to the

agent

Google Translatecloud serviceprovides chat

translation services

Google Translatecloud serviceprovides chat

translation services

Project Approach

Typical project course in data science projects

Iterative Workflow

project launch

Vagueness,

descreasing with

project progress

SolutionMeasureable,

partial results

Scope for

decision making

Actual course

of project

Initally

anticipated

solution

decision gate

Bot design documentation

Proof of Concept(PoC) build-up

Bot use case design workshop

decision gate

Test in production& PoC fine-tuning(multiple Sprints)

Productionfinalization

& integration

Project management, reporting, feedback and documentation

From wish to solution…

Where to start….?

• Many bots consume cloud services, initiate the data security approval within your

company to become “cloud ready”.

• Collect data for the bot training and classify the data.

• Tagged/classified chat transcripts

• Tagged/classified emails

• Frequently asked questions (FAQ) lists

• Knowledgebase articles

• Create a business case for your bot use case to justify the new technology.

• Allocate IT budget for:

• pilots and innovation proof of concepts.

• the production rollout itself.

Webinar Series Spring 2020

• March 03, 2020Extensive functionalities with Cisco's smart omni-channel contact centersCisco Contact Center full coverage with live voting and demos. Determine the topics; from the agent desktop strategy to Cloud CC and enriched AI contact centers.

• March 04, 2020Live Vote! Your top 3 Omnichannel Desktop CRM Integration Challenges and how to approach them#Salesforce #Dynamics #ServiceNow #SAP #Oracle #OwnCRM

• March 12, 2020Home Office: Mobile Agent in Action.

This is a technical Webinar. We will demonstrate what the mobile agent function offers and how you can configure it yourself.

• March 19, 2020 | 09:00amWhat I need to know about bots.In this webinar, you will learn how voice and chatbots work. What are the differences between them? You’ll learn all about intents and entities. Which solutions are available today?

• March 24, 2020 | 09:00am

What's new in CCE 12.5?Learn more about Finesse 12.5 features. What is the SMC Cockpit? Do you already know the new “Cisco Analyzer” reporting engine?

• April 02, 2020 | 09:00amWhat's new in the Cisco omnichannel suite? What is Enterprise Chat and Email (ECE)? How can you extend Cisco ECE with more features?

• April 16, 2020 | 09:00amNews around SMC 7.0. Better understand where SMC can help you in your daily business as a supervisor. What is our Supervisor Management Console vision?

-> Please share these webinars with colleagues